dplyr (version 0.7.6)

summarise_all: Summarise and mutate multiple columns.


These verbs are scoped variants of summarise(), mutate() and transmute(). They apply operations on a selection of variables.

  • summarise_all(), mutate_all() and transmute_all() apply the functions to all (non-grouping) columns.

  • summarise_at(), mutate_at() and transmute_at() allow you to select columns using the same name-based select_helpers just like with select().

  • summarise_if(), mutate_if() and transmute_if() operate on columns for which a predicate returns TRUE.


summarise_all(.tbl, .funs, ...)

summarise_if(.tbl, .predicate, .funs, ...)

summarise_at(.tbl, .vars, .funs, ..., .cols = NULL)

summarize_all(.tbl, .funs, ...)

summarize_if(.tbl, .predicate, .funs, ...)

summarize_at(.tbl, .vars, .funs, ..., .cols = NULL)

mutate_all(.tbl, .funs, ...)

mutate_if(.tbl, .predicate, .funs, ...)

mutate_at(.tbl, .vars, .funs, ..., .cols = NULL)

transmute_all(.tbl, .funs, ...)

transmute_if(.tbl, .predicate, .funs, ...)

transmute_at(.tbl, .vars, .funs, ..., .cols = NULL)



A tbl object.


List of function calls generated by funs(), or a character vector of function names, or simply a function.

Bare formulas are passed to rlang::as_function() to create purrr-style lambda functions. Note that these lambda prevent hybrid evaluation from happening and it is thus more efficient to supply functions like mean() directly rather than in a lambda-formula.


Additional arguments for the function calls in .funs. These are evaluated only once, with tidy dots support.


A predicate function to be applied to the columns or a logical vector. The variables for which .predicate is or returns TRUE are selected. This argument is passed to rlang::as_function() and thus supports quosure-style lambda functions and strings representing function names.


A list of columns generated by vars(), a character vector of column names, a numeric vector of column positions, or NULL.


This argument has been renamed to .vars to fit dplyr's terminology and is deprecated.


A data frame. By default, the newly created columns have the shortest names needed to uniquely identify the output. To force inclusion of a name, even when not needed, name the input (see examples for details).

See Also

vars(), funs()


Run this code
# The scoped variants of summarise() and mutate() make it easy to
# apply the same transformation to multiple variables:

iris %>%
  group_by(Species) %>%

# There are three variants.
# * _all affects every variable
# * _at affects variables selected with a character vector or vars()
# * _if affects variables selected with a predicate function:

# The _at() variants directly support strings:
starwars %>% summarise_at(c("height", "mass"), mean, na.rm = TRUE)

# You can also supply selection helpers to _at() functions but you have
# to quote them with vars():
iris %>% mutate_at(vars(matches("Sepal")), log)
starwars %>% summarise_at(vars(height:mass), mean, na.rm = TRUE)

# The _if() variants apply a predicate function (a function that
# returns TRUE or FALSE) to determine the relevant subset of
# columns. Here we apply mean() to the numeric columns:
starwars %>% summarise_if(is.numeric, mean, na.rm = TRUE)

# mutate_if() is particularly useful for transforming variables from
# one type to another
iris %>% as_tibble() %>% mutate_if(is.factor, as.character)
iris %>% as_tibble() %>% mutate_if(is.double, as.integer)

# ---------------------------------------------------------------------------
# If you want apply multiple transformations, use funs()
by_species <- iris %>% group_by(Species)

by_species %>% summarise_all(funs(min, max))
# Note that output variable name now includes the function name, in order to
# keep things distinct.

# You can express more complex inline transformations using .
by_species %>% mutate_all(funs(. / 2.54))

# Function names will be included if .funs has names or multiple inputs
by_species %>% mutate_all(funs(inches = . / 2.54))
by_species %>% summarise_all(funs(med = median))
by_species %>% summarise_all(funs(Q3 = quantile), probs = 0.75)
by_species %>% summarise_all(c("min", "max"))
# }

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